Incidence and predictors of case cancellation within 24 h in patients scheduled for elective surgical procedures

Wongtangman, Karuna; Azimaraghi, Omid; Freda, Jeffrey; Ganz-Lord, Fran; Shamamian, Peter; Bastien, Alexandra; Mirhaji, Parsa; Himes, Carina P.; Rupp, Samuel; Green-Lorenzen, Susan; Smith, Richard V.; Medrano, Elilary Montilla; Anand, Preeti; Rego, Simon A.; Velji, Salimah; Eikermann, Matthias

doi:10.1016/j.jclinane.2022.110987

Cited by 11 publications

(13 citation statements)

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“…provided a roadmap for successful and sustained implementation of an interdisciplinary, collaborative programme for pre-operative assessment consisting of three parts: routine analysis of existing patient data; the addition of standardised screening questions to triage patients at the time of surgical planning; and the implementation of a `perioperative enhancement team´to optimise the management of certain chronic conditions known to impact postoperative outcomes before surgery [35]. Our intervention was inspired by these innovations but included the addition of validated prediction tools to automatically estimate the same-day case rate and ASA physical status from EHR data for use by surgeons, anaesthetists and other clinicians involved in peri-operative care [21][22][23].…”

Section: Discussionmentioning

confidence: 99%

“…We created and validated an instrument to predict the risk of case cancellation within 24 h and automated the estimation of ASA physical status through the use of machine learning [21][22][23]. Both scores were available at the time of case booking and identified high-risk patients who required more comprehensive pre-operative assessment and risk stratification in advance of surgery.…”

Section: Methodsmentioning

confidence: 99%

“…Both scores were available at the time of case booking and identified high-risk patients who required more comprehensive pre-operative assessment and risk stratification in advance of surgery. In addition, the surgeon also documented each patient's answers to four essential questions related to: medical comorbidities; hospitalisation within the past 3 months; exercise tolerance; and history of prior anaesthetic complications (online Supporting Information Appendix S2) [21]. These instruments were implemented through a new automated EHR workflow and launched in April 2022.…”

Section: Methodsmentioning

confidence: 99%

“…The primary outcome was same‐day case cancellation within the otorhinolaryngology service, defined as cancellation of an elective surgery within 24 h of the scheduled time. We used multivariable logistic regression to evaluate the potential association between bundle implementation and the primary outcome, controlling for published predictors of same‐day case cancellation [21]. We conducted interrupted time‐series analysis to examine the time‐dependency of this association and used segmented regression to evaluate immediate (level) changes in the outcome rate as well as changes in the trend (slope) [27].…”

Section: Methodsmentioning

confidence: 99%

“…The interprofessional quality and safety group at our institution created and validated a tool to predict case cancellation within 24 h for elective surgical procedures [21,22] and a machine learning instrument that allows surgeons to reliably estimate ASA physical status early in the process of preparing a patient for surgery [23]. We report the implementation of this bundle intervention in the ambulatory otorhinolaryngology surgical population at a single centre.…”

Section: Introductionmentioning

confidence: 99%

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The effect of a bundle intervention for ambulatory otorhinolaryngology procedures on same‐day case cancellation rate and associated costs

Wongtangman,

Semczuk,

Freda

et al. 2024

Anaesthesia

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SummaryCancellations within 24 h of planned elective surgical procedures reduce operating theatre efficiency, add unnecessary costs and negatively affect patient experience. We implemented a bundle intervention that aimed to reduce same‐day case cancellations. This consisted of communication tools to improve patient engagement and new screening instruments (automated estimation of ASA physical status and case cancellation risk score plus four screening questions) to identify patients in advance (ideally before case booking) who needed comprehensive pre‐operative risk stratification. We studied patients scheduled for ambulatory surgery with the otorhinolaryngology service at a single centre from April 2021 to December 2022. Multivariable logistic regression and interrupted time‐series analyses were used to analyse the effects of this intervention on case cancellations within 24 h and costs. We analysed 1548 consecutive scheduled cases. Cancellation within 24 h occurred in 114 of 929 (12.3%) cases pre‐intervention and 52 of 619 (8.4%) cases post‐intervention. The cancellation rate decreased by 2.7% (95%CI 1.6–3.7%, p < 0.01) during the first month, followed by a monthly decrease of 0.2% (95%CI 0.1–0.4%, p < 0.01). This resulted in an estimated $150,200 (£118,755; €138,370) or 35.3% cost saving (p < 0.01). Median (IQR [range]) number of days between case scheduling and day of surgery decreased from 34 (21–61 [0–288]) pre‐intervention to 31 (20–51 [1–250]) post‐intervention (p < 0.01). Patient engagement via the electronic health record patient portal or text messaging increased from 75.9% at baseline to 90.8% (p < 0.01) post‐intervention. The primary reason for case cancellation was patients' missed appointment on the day of surgery, which decreased from 7.2% pre‐intervention to 4.5% post‐intervention (p = 0.03). An anaesthetist‐driven, clinical informatics‐based bundle intervention decreases same‐day case cancellation rate and associated costs in patients scheduled for ambulatory otorhinolaryngology surgery.

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